Master of Science
Wind ramp events have a significant influence of uncertainty in wind power production. In order to build an efficient decision-making systems for the smart grid, developing statistical models based on analysis of historical data of wind ramp events is indispensable. In this paper, we design a detection algorithm to analyze historical data, build distribution models to predict and simulate wind ramp events. Phase-type distribution consists of a convolution of the Exponential distribution which can be used to apply Markov decisions process and identify the factors which can cause wind ramp events. We use three types of Phase-type distribution to fit the data sets of duration, obtain the optimal number of phases and the parameters. Both the model of simulation and Phase-type distribution can be used to help making decisions and improving the accuracy of forecast for wind power production in smart grid.
Du, Xingbang, "Detection and Modeling of Wind Ramp Events in Smart Grid" (2018). Theses and Dissertations. 4276.